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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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A Study on Prediction of Business Status Based on Machine Learning

인공지능연구 / Korean Journal of Artificial Intelligence, (E)2508-7894
2018, v.6 no.2, pp.23-27
https://doi.org/https://doi.org/10.24225/kjai.2018.6.2.23
Kim, Ki-Pyeong (Daejeon University)
Song, Seo-Won (Dept. of Medical IT Marketing, Eulji University)

Abstract

Korea has a high proportion of self-employment. Many of them start the food business since it does not require high-techs and it is possible to start the business relatively easily compared to many others in business categories. However, the closure rate of the business is also high due to excessive competition and market saturation. Cafés and restaurants are examples of food business where the business analysis is highly important. However, for most of the people who want to start their own business, it is difficult to conduct systematic business analysis such as trade area analysis or to find information for business analysis. Therefore, in this paper, we predicted business status with simple information using Microsoft Azure Machine Learning Studio program. Experimental results showed higher performance than the number of attributes, and it is expected that this artificial intelligence model will be helpful to those who are self-employed because it can easily predict the business status. The results showed that the overall accuracy was over 60 % and the performance was high compared to the number of attributes. If this model is used, those who prepare for self-employment who are not experts in the business analysis will be able to predict the business status of stores in Seoul with simple attributes.

keywords
Food Hygiene Business, Business Analysis, Machine Learning, Prediction of Business Status, Neural Network

인공지능연구